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import torch.nn as nn
import torch.nn.functional as F
class TFBatchNorm2d(nn.Module):
"""BatchNorm compatible with the original TF graph's training=True usage."""
def __init__(self, channels, eps=1e-5, momentum=0.99):
super().__init__()
self.eps = eps
self.momentum = momentum
self.weight = nn.Parameter(torch.ones(channels))
self.bias = nn.Parameter(torch.zeros(channels))
self.register_buffer("running_mean", torch.zeros(channels))
self.register_buffer("running_var", torch.ones(channels))
def forward(self, x):
mean = x.mean(dim=(0, 2, 3), keepdim=True)
var = (x - mean).pow(2).mean(dim=(0, 2, 3), keepdim=True)
if self.training:
with torch.no_grad():
self.running_mean.mul_(self.momentum).add_(
mean.reshape(-1), alpha=1.0 - self.momentum
)
self.running_var.mul_(self.momentum).add_(
var.reshape(-1), alpha=1.0 - self.momentum
)
weight = self.weight.view(1, -1, 1, 1)
bias = self.bias.view(1, -1, 1, 1)
return (x - mean) * torch.rsqrt(var + self.eps) * weight + bias
class Generator(nn.Module):
def __init__(self, input_channels=3, batch_norm_eps=1e-5):
super().__init__()
filters = [64, 128, 256, 512, 512, 512, 512, 512, 512, 512, 512, 512, 256, 128, 64]
self.down_convs = nn.ModuleList()
in_channels = input_channels
for out_channels in filters[:8]:
self.down_convs.append(
nn.Conv2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1)
)
in_channels = out_channels
self.down_bns = nn.ModuleList(
[TFBatchNorm2d(channels, eps=batch_norm_eps) for channels in filters[1:8]]
)
self.up_convs = nn.ModuleList()
up_in_channels = [
filters[7],
filters[8] + filters[6],
filters[9] + filters[5],
filters[10] + filters[4],
filters[11] + filters[3],
filters[12] + filters[2],
filters[13] + filters[1],
filters[14] + filters[0],
]
up_out_channels = filters[8:] + [input_channels]
for in_channels, out_channels in zip(up_in_channels, up_out_channels):
self.up_convs.append(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1)
)
self.up_bns = nn.ModuleList(
[TFBatchNorm2d(channels, eps=batch_norm_eps) for channels in filters[8:15]]
)
self.apply(_initialize_layer)
def forward(self, x):
original = x
layers = []
y = self.down_convs[0](x)
layers.append(y)
for i in range(1, 8):
y = F.leaky_relu(layers[-1], negative_slope=0.2)
y = self.down_convs[i](y)
y = self.down_bns[i - 1](y)
layers.append(y)
y = F.relu(layers[-1])
y = self.up_convs[0](y)
y = self.up_bns[0](y)
layers.append(y)
for up_index, skip_index in enumerate([6, 5, 4, 3, 2, 1], start=1):
y = torch.cat([layers[-1], layers[skip_index]], dim=1)
y = F.relu(y)
y = self.up_convs[up_index](y)
y = self.up_bns[up_index](y)
layers.append(y)
y = torch.cat([layers[-1], layers[0]], dim=1)
y = F.relu(y)
y = self.up_convs[7](y)
y = F.relu(y)
return original - y
class Discriminator(nn.Module):
def __init__(self, input_channels=3, batch_norm_eps=1e-3):
super().__init__()
filters = [32, 64, 64, 128, 128, 256, 256, 256, 8]
strides = [1, 2, 1, 2, 1, 2, 1, 2, 2]
self.convs = nn.ModuleList()
in_channels = input_channels
for index, (out_channels, stride) in enumerate(zip(filters, strides)):
padding = 1 if stride == 1 else 0
self.convs.append(
nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=padding,
)
)
in_channels = out_channels
self.batch_norms = nn.ModuleList(
[TFBatchNorm2d(channels, eps=batch_norm_eps) for channels in filters[1:]]
)
self.dense = nn.Conv2d(filters[-1], 1, kernel_size=1)
self.apply(_initialize_layer)
def forward(self, x):
features = []
y = F.leaky_relu(self.convs[0](x), negative_slope=0.2)
features.append(y)
for index in range(1, len(self.convs)):
y = self.convs[index](features[-1])
y = self.batch_norms[index - 1](y)
y = F.leaky_relu(y, negative_slope=0.2)
features.append(y)
prediction = torch.sigmoid(self.dense(features[-1]))
return (*features[:8], prediction)
def _initialize_layer(module):
if isinstance(module, (nn.Conv2d, nn.ConvTranspose2d)):
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
def create_generator(
weights_path=None,
device=None,
input_channels=3,
batch_norm_eps=1e-5,
):
device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = Generator(
input_channels=input_channels,
batch_norm_eps=batch_norm_eps,
).to(device)
if weights_path:
checkpoint = torch.load(weights_path, map_location=device)
state_dict = checkpoint.get("state_dict", checkpoint)
net.load_state_dict(state_dict)
net.eval()
return net
def create_discriminator(
weights_path=None,
device=None,
input_channels=3,
batch_norm_eps=1e-3,
):
device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = Discriminator(
input_channels=input_channels,
batch_norm_eps=batch_norm_eps,
).to(device)
if weights_path:
checkpoint = torch.load(weights_path, map_location=device)
state_dict = checkpoint.get("state_dict", checkpoint)
net.load_state_dict(state_dict)
net.eval()
return net
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